Snowflake Data Engineer

Key responsibilities

• Design, build, test, deploy, and maintain scalable data pipelines, transformations, and data models on Snowflake.
• Develop and productionize AI and machine learning solutions using Snowflake Cortex AI, including AI agents, retrieval and search experiences, and external-facing applications.
• Create, validate, version, and maintain semantic views and related business logic to support accurate, governed AI and analytics experiences.
• Build and support ingestion patterns using Snowflake Openflow, Kafka, Snowpipe, APIs, and third-party connectors, including integrations with enterprise applications such as NetSuite.
• Develop reusable engineering components and automation using advanced SQL, SnowSQL or Snowflake CLI, Python, and, where appropriate, JavaScript.
• Implement data quality checks, automated testing, observability, error handling, and deployment controls to ensure reliable production data and AI workloads.
• Optimize data models, queries, pipelines, and AI workloads for performance, maintainability, scalability, and efficient consumption.
• Design and implement secure Snowflake data sharing and data-product patterns for internal teams, partners, and customers.
• Establish software engineering practices for Snowflake development, including source control, CI/CD, environment promotion, documentation, and release management.
• Troubleshoot pipeline, application, semantic-layer, and data-quality issues and drive them through resolution.
• Partner with the Snowflake Account Administrator and security teams on required roles, privileges, SSO/SCIM integration, governance policies, and production readiness without assuming ownership of account administration.
• Collaborate with data engineering, analytics, application, product, and business teams to translate high-value use cases into durable data and AI solutions.

Required experience

• Prior hands-on experience as a Snowflake data engineer, AI engineer, analytics engineer, or similar role delivering production solutions on Snowflake.
• Strong Snowflake SQL skills and demonstrated experience developing data pipelines, transformations, data models, and performance-tuned workloads.
• Experience building and maintaining applications or AI/ML solutions with Snowflake Cortex AI or comparable large language model and machine learning technologies.
• Experience designing semantic layers or semantic views that translate business concepts into governed, reusable definitions for analytics and AI.
• Proficiency in Python and experience applying software engineering practices such as testing, source control, CI/CD, and code review.
• Experience with streaming, event-driven, or connector-based ingestion using technologies such as Kafka, Openflow, Snowpipe, APIs, or comparable integration frameworks.
• Experience supporting production data platforms on Azure and integrating with enterprise identity and security patterns.
• Ability to diagnose and resolve complex data, pipeline, application, and performance issues.
• Ability to work cross-functionally with platform administration, security, data engineering, analytics, application, and business teams.

Preferred qualifications

• Experience creating, deploying, evaluating, and maintaining AI agents or generative AI applications in production.
• Experience with Snowflake Cortex Analyst, Cortex Search, Snowpark, Streamlit in Snowflake, or Snowflake-native application development.
• Experience with Snowflake data sharing, Marketplace listings, secure data products, or customer-facing data delivery patterns.
• Experience implementing Openflow or Kafka-based ingestion and integrating SaaS or ERP sources such as NetSuite.
• Experience with Azure services, Azure AD SSO/SCIM integration, and secure cloud application patterns.
• Experience working with SQL Server in a hybrid SQL Server and Snowflake environment.
• JavaScript development experience is a plus.
• Snowflake certifications are a plus.
• Experience supporting regulated, payment, financial-services, or other security-sensitive environments is strongly preferred.

What success looks like

• Reliable, scalable data pipelines and data products move from development to production with strong quality and observability.
• AI agents and applications deliver accurate, governed, measurable value to internal users and external customers.
• Semantic views remain trusted, testable, and resilient as underlying data and business definitions evolve.
• New data sources and streaming workloads are integrated efficiently using maintainable, reusable engineering patterns.
• Data and AI workloads are performant, cost-aware, documented, and supported through disciplined software delivery practices.
• Engineering teams can move quickly while account administration, security, and governance responsibilities remain clearly separated and well coordinated.